Construction of benchmark retinal image database for diabetic retinopathy analysis. (September 2020)

• Record Type: Journal Article
• Authors: Kaur, Jaskirat; Mittal, Deepti
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 234:Number 9(2020)
• Page Start: 1036
• Page End: 1048
• Publication Date: 2020-09
• Subjects: Diabetic retinopathy -- retinal images -- benchmark database -- annotations; Biomedical engineering -- Periodicals; Medical instruments and apparatus -- Periodicals; 610.28
• DOI: 10.1177/0954411920938569
• Languages: English
• ISSNs: 0954-4119

Abstract

Diabetic retinopathy, a symptomless medical condition of diabetes, is one of the significant reasons of vision impairment all over the world. The prior detection and diagnosis can decrease the occurrence of acute vision loss and enhance efficiency of treatment. Fundus imaging, a non-invasive diagnostic technique, is the most frequently used mode for analyzing retinal abnormalities related to diabetic retinopathy. Computer-aided methods based on retinal fundus images support quick diagnosis, impart an additional perspective during decision-making, and behave as an efficient means to assess response of treatment on retinal abnormalities. However, in order to evaluate computer-aided systems, a benchmark database of clinical retinal fundus images is required. Therefore, a representative database comprising of 2942 clinical retinal fundus images is developed and presented in this work. This clinical database, having varying attributes such as position, dimensions, shapes, and color, is formed to evaluate the generalization capability of computer-aided systems for diabetic retinopathy diagnosis. A framework for the development of benchmark retinal fundus images database is also proposed. The developed database comprises of medical image annotations for each image from expert ophthalmologists corresponding to anatomical structures, retinal lesions and stage of diabetic retinopathy. In addition, the substantial performance comparison capability of the proposed database aids in analyzing candidature of different methods, and subsequently its usage in medical practice for real-time applications.